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Baseado na classificação ocupacional SOC
Canonical b00t reviewer capability — adversarial multi-framework code/datum review. Shared across all harnesses (Claude Code, opencode, Hermes, b00t-cli). Loaded via: b00t learn reviewer, or by harness-specific role supplements.
Remove AI-generated artifacts from code. Three-phase certainty-graded cleanup. Use after any AI implementation session or before PR creation.
8-phase agile workflow — strategy→ideate→brainstorm→plan→work→review→compound→pulse. State machine with FOL-guarded transitions, gh-issues backlog, executable just harness. Inspired by everyinc/compound-engineering-plugin.
Australian crypto tax treatment per ATO guidance QC 53725. Crypto assets are CGT assets under ITAA 1997 s 108-5. Each disposal triggers CGT event A1. Covers personal-use exception, trading stock test, and the 50% CGT discount.
Australian R&D Tax Incentive (RDTI) — ITAA 1997 Division 355. Covers eligibility criteria for core and supporting R&D activities, registration with AusIndustry/IP Australia, expenditure categories, offset rates, and the company size threshold test.
Evidence graph construction for tax audit trails. Each Satisfies check emits EvidenceNode structs with Blake3 content hashes. Evidence chains are tamper-evident and link legislative citations to factual findings.
| name | sia |
| description | |- |
SIA (Self Improving AI) implements a 3-agent loop — Meta, Target, and Feedback — for autonomous benchmark improvement. The Meta agent designs task-specific agents, the Target agent executes tasks, and the Feedback agent reviews performance and drives improvement. Results include 56.6% LawBench gain, #1 on MLE-Bench, and 14x GPU kernel speedup. The three agent roles map to b00t patterns: Meta ≈ research-soul, Feedback ≈ lfmf + discriminator, and generations ≈ checkpoints.
Installation is via pip install 'sia-agent[claude]' or pip install 'sia-agent[openhands]'. Run with sia run --task gpqa --max_gen 5 --run_id 1. Visualize results with sia web. Custom tasks use sia run --task_dir ./my-task --max_gen 5 --run_id 1. MLE-Bench requires dataset preparation via python -m sia.prepare_mlebench_dataset -c 'spaceship-titanic'.
Use SIA for autonomous task improvement, benchmark optimization, and any scenario requiring a feedback-driven self-improvement loop. It is particularly effective for ML benchmarks and agent optimization tasks.
pip install 'sia-agent[claude]'sia run --task gpqa --max_gen 5 --run_id 1sia websia run --task_dir ./my-task --max_gen 5 --run_id 1python -m sia.prepare_mlebench_dataset -c 'spaceship-titanic'